EEG Feature Selection Using Mutual Information and Support Vector Machine: A Comparative Analysis
(2010) 32nd Annual International IEEE EMBC Conference (EMBC 2010) — Location: Buenos Aires (Argentina) (31.August.2010)
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- Abstract
- The large number of methods for EEG feature extraction demands a good choice for EEG features for every task. This paper compares three subsets of features obtained by tracks extraction method, wavelet transform and fractional Fourier transform. Particularly, we compare the performance of each subset in classification tasks using support vector machines and then we select possible combination of features by feature selection methods based on forward–backward procedure and mutual information as relevance criteria. Results confirm that fractional Fourier transform coefficients present very good performance and also the possibility of using some combination of this feature to improve the performance of the classifier. To reinforce the relevance of the study, we carry out 1000 independent runs using a bootstrap approach, and evaluate the statistical significance of the Fscore results using the Kruskal-Wallis test.
- Affiliations
Universidad Carlos III de Madrid
Citations
Guerrero-Mosquera, C., Verleysen, M., & Navia Vazquez, A. (2010). EEG Feature Selection Using Mutual Information and Support Vector Machine: A Comparative Analysis. Proceedings of the 32nd Annual International IEEE EMBC Conference (EMBC 2010), p. 4946-4949. https://hdl.handle.net/2078.5/254187